1,614 research outputs found
Digital transformation: A multidisciplinary perspective and future research agenda
Digital transformation has had an unprecedented influence on all sectors of business over the last decade. We are now entering an era characterized by the extensive digital transformation of businesses, society, and consumers. Therefore, digital transformation has become a pivotal focus for organizations across various sectors in recent years. Despite differing scholarly perspectives on the concept and elements of digital transformation, a consensus exists that it significantly impacts consumer decisions and necessitates organizational adaptation. Recent challenges such as the COVID‐19 pandemic have further accelerated the need for digital transformation and its effects on consumers. This necessitates an editorial perspective on this most important topic to establish future research agenda encompassing the various dimensions of digital transformation. The purpose of this editorial perspective is to review research on digital transformation from a multidisciplinary viewpoint and provide insights into several key domains—Internet‐of‐Things, social media, mobile apps, artificial intelligence, augmented and virtual reality, the metaverse, and corporate digital responsibility—that are poised to fuel the pace of digital transformation. Each domain is analyzed through a lens of introduction, role, importance, multifaceted impact, and conclusions. Future research directions are suggested
Self-supervised learning for transferable representations
Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks
IRGen: Generative Modeling for Image Retrieval
While generative modeling has been ubiquitous in natural language processing
and computer vision, its application to image retrieval remains unexplored. In
this paper, we recast image retrieval as a form of generative modeling by
employing a sequence-to-sequence model, contributing to the current unified
theme. Our framework, IRGen, is a unified model that enables end-to-end
differentiable search, thus achieving superior performance thanks to direct
optimization. While developing IRGen we tackle the key technical challenge of
converting an image into quite a short sequence of semantic units in order to
enable efficient and effective retrieval. Empirical experiments demonstrate
that our model yields significant improvement over three commonly used
benchmarks, for example, 22.9\% higher than the best baseline method in
precision@10 on In-shop dataset with comparable recall@10 score
Data-assisted modeling of complex chemical and biological systems
Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter
A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms
Autonomous navigation has become a widely researched area of expertise over the past few years, gaining a massive following due to its necessity in creating a fully autonomous robotic system. Autonomous navigation is an exceedingly difficult task to accomplish in and of itself. Successful navigation relies heavily on the ability to self-localise oneself within a given environment. Without this awareness of one’s
own location, it is impossible to successfully navigate in an autonomous manner. Since its inception Simultaneous Localization and Mapping (SLAM) has become one of the most widely researched areas of autonomous navigation. SLAM focuses on self-localization within a mapped or un-mapped environment, and constructing or updating the map of one’s surroundings. Visual Place Recognition (VPR) is an essential part of any SLAM system. VPR relies on visual cues to determine one’s location within a mapped environment.
This thesis presents two main topics within the field of VPR. First, this thesis presents a benchmark analysis of several popular embedded platforms when performing VPR. The presented benchmark analyses six different VPR techniques
across three different datasets, and investigates accuracy, CPU usage, memory usage, processing time and power consumption. The benchmark demonstrated a clear relationship between platform architecture and the metrics measured, with platforms of the same architecture achieving comparable accuracy and algorithm efficiency.
Additionally, the Raspberry Pi platform was noted as a standout in terms of algorithm efficiency and power consumption.
Secondly, this thesis proposes an evaluation framework intended to provide information about a VPR technique’s useability within a real-time application. The approach
makes use of the incoming frame rate of an image stream and the VPR frame rate, the rate at which the technique can perform VPR, to determine how efficient VPR techniques would be in a real-time environment. This evaluation framework determined that CoHOG would be the most effective algorithm to be deployed in a real-time environment as it had the best ratio between computation time and accuracy
Linking Representations with Multimodal Contrastive Learning
Many applications require grouping instances contained in diverse document
datasets into classes. Most widely used methods do not employ deep learning and
do not exploit the inherently multimodal nature of documents. Notably, record
linkage is typically conceptualized as a string-matching problem. This study
develops CLIPPINGS, (Contrastively Linking Pooled Pre-trained Embeddings), a
multimodal framework for record linkage. CLIPPINGS employs end-to-end training
of symmetric vision and language bi-encoders, aligned through contrastive
language-image pre-training, to learn a metric space where the pooled
image-text representation for a given instance is close to representations in
the same class and distant from representations in different classes. At
inference time, instances can be linked by retrieving their nearest neighbor
from an offline exemplar embedding index or by clustering their
representations. The study examines two challenging applications: constructing
comprehensive supply chains for mid-20th century Japan through linking firm
level financial records - with each firm name represented by its crop in the
document image and the corresponding OCR - and detecting which image-caption
pairs in a massive corpus of historical U.S. newspapers came from the same
underlying photo wire source. CLIPPINGS outperforms widely used string matching
methods by a wide margin and also outperforms unimodal methods. Moreover, a
purely self-supervised model trained on only image-OCR pairs also outperforms
popular string-matching methods without requiring any labels
Reconstruction and Synthesis of Human-Scene Interaction
In this thesis, we argue that the 3D scene is vital for understanding, reconstructing, and synthesizing human motion. We present several approaches which take the scene into consideration in reconstructing and synthesizing Human-Scene Interaction (HSI). We first observe that state-of-the-art pose estimation methods ignore the 3D scene and hence reconstruct poses that are inconsistent with the scene. We address this by proposing a pose estimation method that takes the 3D scene explicitly into account. We call our method PROX for Proximal Relationships with Object eXclusion. We leverage the data generated using PROX and build a method to automatically place 3D scans of people with clothing in scenes. The core novelty of our method is encoding the proximal relationships between the human and the scene in a novel HSI model, called POSA for Pose with prOximitieS and contActs. POSA is limited to static HSI, however. We propose a real-time method for synthesizing dynamic HSI, which we call SAMP for Scene-Aware Motion Prediction. SAMP enables virtual humans to navigate cluttered indoor scenes and naturally interact with objects. Data-driven kinematic models, like SAMP, can produce high-quality motion when applied in environments similar to those shown in the dataset. However, when applied to new scenarios, kinematic models can struggle to generate realistic behaviors that respect scene constraints. In contrast, we present InterPhys which uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a physical and life-like manner
Hierarchical Classification of Design Decisions using pre-trained Language Models
Die Software-Architektur Dokumentation (SAD) ist ein integrales Artefakt eines Software Projektes. Die SAD trägt zum fortwährenden Erfolg eines Software Projektes bei, indem sie ein gemeinsames Verständnis der Software Architektur gewährleistet, wichtige Entwurfsentscheidungen dokumentiert und einer Erosion der Software vorbeugt. Um die Qualität von SADs zu verbessern und nachgelagerte Aufgaben zu unterstützen, ist eine automatische Klassifizierung dieser Entwurfsentscheidungen erstrebenswert. In dieser Arbeit implementieren und evaluieren wir einen Ansatz zur automatischen Identifikation und Klassifizierung von Entwurfsentscheidungen auf der Grundlage einer feingranularen Taxonomie, bei der wir eine hierarchische Klassifikationsstrategie mit dem Einsatz von Transfer-Lernen durch vortrainierter Sprachmodelle kombinieren. Der Beitrag dieser Arbeit besteht darin, den Vorteil einer hierarchischen Klassifikationsstrategie für die automatische Klassifikation von Entwurfsentscheidungen gegenüber einem nicht-hierarchischen Ansatz zu untersuchen. Außerdem untersuchen und vergleichen wir die Effektivität der vortrainierten Sprachmodelle RoBERTa, XLNet, BERTOverflow und GPT-3 für diese Aufgabe. In unserer Evaluation schnitten die Ansätze mit vortrainierten Sprachmodellen im Allgemeinen besser ab als die Baseline-Ansätze. Wir konnten jedoch keinen klaren Vorteil der hierarchischen Ansätze gegenüber den nicht-hierarchischen Ansätzen in Kombination mit den Sprachmodellen feststelle. Letztlich sind die Ergebnisse dieser Arbeit durch die Größe und das Ungleichgewicht unseres Datensatzes limitiert und erfordern daher weitere Forschung mit einem größeren Datensatz
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